from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
scikit-learn vs. scikit-learn-intelex (Intel® oneAPI) benchmarks: perfect hyperparameters match¶print_time_report()
sklearnex_KMeans_short: 0h 0m 2s
sklearnex_Ridge: 0h 0m 2s
KMeans_short: 0h 0m 3s
sklearnex_LogisticRegression: 0h 0m 6s
sklearnex_KMeans_tall: 0h 0m 11s
Ridge: 0h 0m 12s
KMeans_tall: 0h 0m 27s
LogisticRegression: 0h 0m 27s
sklearnex_KNeighborsClassifier_kd_tree: 0h 0m 32s
KNeighborsClassifier_kd_tree: 0h 2m 37s
catboost_symmetric: 0h 5m 5s
lightgbm: 0h 5m 9s
xgboost: 0h 5m 12s
HistGradientBoostingClassifier: 0h 5m 12s
catboost_lossguide: 0h 5m 18s
sklearnex_KNeighborsClassifier: 0h 6m 26s
KNeighborsClassifier: 0h 34m 34s
total: 1h 11m 42s
print_env_info()
{
"system_info": {
"python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.8.0-1036-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1.3",
"setuptools": "49.6.0.post20210108",
"sklearn": "1.0.dev0",
"numpy": "1.21.0",
"scipy": "1.7.0",
"Cython": null,
"pandas": "1.3.0",
"matplotlib": "3.4.2",
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
"prefix": "libopenblas",
"user_api": "blas",
"internal_api": "openblas",
"version": "0.3.15",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
}
],
"cpu_count": 2
}
reporting = Reporting(config="config.yml")
reporting.run()
KNeighborsClassifier: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)¶All estimators share the following hyperparameters: algorithm=brute.
| estimator | function | n_samples_train | n_samples | n_features | dataset_n_classes | dataset_n_redundant | dataset_random_state | dataset_sample_generator | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_sklearnex | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.156 | 0.0 | 5.131 | 0.0 | 1 | 1 | NaN | NaN | 0.568 | 0.0 | 0.274 | 0.0 | See | See |
| 3 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.140 | 0.0 | 5.699 | 0.0 | 1 | 100 | NaN | NaN | 0.565 | 0.0 | 0.248 | 0.0 | See | See |
| 6 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.145 | 0.0 | 5.532 | 0.0 | -1 | 1 | NaN | NaN | 0.545 | 0.0 | 0.265 | 0.0 | See | See |
| 9 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.140 | 0.0 | 5.702 | 0.0 | 1 | 5 | NaN | NaN | 0.552 | 0.0 | 0.254 | 0.0 | See | See |
| 12 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.137 | 0.0 | 5.849 | 0.0 | -1 | 100 | NaN | NaN | 0.553 | 0.0 | 0.247 | 0.0 | See | See |
| 15 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.139 | 0.0 | 5.771 | 0.0 | -1 | 5 | NaN | NaN | 0.547 | 0.0 | 0.254 | 0.0 | See | See |
| 18 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.055 | 0.0 | 0.289 | 0.0 | 1 | 1 | NaN | NaN | 0.099 | 0.0 | 0.559 | 0.0 | See | See |
| 21 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.057 | 0.0 | 0.279 | 0.0 | 1 | 100 | NaN | NaN | 0.100 | 0.0 | 0.575 | 0.0 | See | See |
| 24 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.054 | 0.0 | 0.297 | 0.0 | -1 | 1 | NaN | NaN | 0.106 | 0.0 | 0.509 | 0.0 | See | See |
| 27 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.061 | 0.0 | 0.261 | 0.0 | 1 | 5 | NaN | NaN | 0.101 | 0.0 | 0.608 | 0.0 | See | See |
| 30 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.061 | 0.0 | 0.262 | 0.0 | -1 | 100 | NaN | NaN | 0.102 | 0.0 | 0.600 | 0.0 | See | See |
| 33 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.059 | 0.0 | 0.272 | 0.0 | -1 | 5 | NaN | NaN | 0.102 | 0.0 | 0.578 | 0.0 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | dataset_n_classes | dataset_n_redundant | dataset_random_state | dataset_sample_generator | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_sklearnex | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 18.329 | 0.260 | 0.0 | 0.018 | 1 | 1 | 0.705 | 0.948 | 4.665 | 0.075 | 3.929 | 0.084 | See | See |
| 2 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.224 | 0.003 | 0.0 | 0.224 | 1 | 1 | 1.000 | 1.000 | 0.107 | 0.003 | 2.090 | 0.062 | See | See |
| 4 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 26.739 | 0.146 | 0.0 | 0.027 | 1 | 100 | 0.925 | 0.948 | 4.649 | 0.040 | 5.751 | 0.059 | See | See |
| 5 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.227 | 0.002 | 0.0 | 0.227 | 1 | 100 | 1.000 | 1.000 | 0.110 | 0.008 | 2.055 | 0.150 | See | See |
| 7 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 31.936 | 0.000 | 0.0 | 0.032 | -1 | 1 | 0.705 | 0.839 | 4.564 | 0.051 | 6.997 | 0.079 | See | See |
| 8 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.207 | 0.020 | 0.0 | 0.207 | -1 | 1 | 1.000 | 1.000 | 0.108 | 0.002 | 1.910 | 0.186 | See | See |
| 10 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 26.767 | 0.179 | 0.0 | 0.027 | 1 | 5 | 0.807 | 0.716 | 4.536 | 0.032 | 5.901 | 0.057 | See | See |
| 11 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.233 | 0.006 | 0.0 | 0.233 | 1 | 5 | 1.000 | 1.000 | 0.107 | 0.002 | 2.165 | 0.066 | See | See |
| 13 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 40.677 | 0.000 | 0.0 | 0.041 | -1 | 100 | 0.925 | 0.839 | 4.552 | 0.041 | 8.936 | 0.081 | See | See |
| 14 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.218 | 0.019 | 0.0 | 0.218 | -1 | 100 | 1.000 | 1.000 | 0.108 | 0.005 | 2.014 | 0.198 | See | See |
| 16 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 40.966 | 0.000 | 0.0 | 0.041 | -1 | 5 | 0.807 | 0.716 | 4.527 | 0.025 | 9.050 | 0.050 | See | See |
| 17 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.219 | 0.021 | 0.0 | 0.219 | -1 | 5 | 1.000 | 1.000 | 0.108 | 0.002 | 2.018 | 0.198 | See | See |
| 19 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 12.361 | 0.071 | 0.0 | 0.012 | 1 | 1 | 0.978 | 0.992 | 1.116 | 0.022 | 11.072 | 0.231 | See | See |
| 20 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.017 | 0.001 | 0.0 | 0.017 | 1 | 1 | 1.000 | 1.000 | 0.005 | 0.000 | 3.249 | 0.313 | See | See |
| 22 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 20.880 | 0.070 | 0.0 | 0.021 | 1 | 100 | 0.986 | 0.992 | 1.103 | 0.014 | 18.923 | 0.244 | See | See |
| 23 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.023 | 0.001 | 0.0 | 0.023 | 1 | 100 | 1.000 | 1.000 | 0.005 | 0.000 | 4.706 | 0.406 | See | See |
| 25 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 25.785 | 0.279 | 0.0 | 0.026 | -1 | 1 | 0.978 | 0.990 | 1.015 | 0.024 | 25.413 | 0.655 | See | See |
| 26 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.023 | 0.003 | 0.0 | 0.023 | -1 | 1 | 1.000 | 1.000 | 0.005 | 0.000 | 4.973 | 0.731 | See | See |
| 28 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 20.840 | 0.138 | 0.0 | 0.021 | 1 | 5 | 0.986 | 0.984 | 1.015 | 0.012 | 20.538 | 0.279 | See | See |
| 29 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.021 | 0.002 | 0.0 | 0.021 | 1 | 5 | 1.000 | 1.000 | 0.005 | 0.000 | 4.455 | 0.504 | See | See |
| 31 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 34.004 | 0.000 | 0.0 | 0.034 | -1 | 100 | 0.986 | 0.990 | 1.023 | 0.022 | 33.232 | 0.705 | See | See |
| 32 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.030 | 0.002 | 0.0 | 0.030 | -1 | 100 | 1.000 | 1.000 | 0.005 | 0.001 | 6.083 | 0.855 | See | See |
| 34 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 34.081 | 0.000 | 0.0 | 0.034 | -1 | 5 | 0.986 | 0.984 | 1.025 | 0.020 | 33.261 | 0.638 | See | See |
| 35 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.031 | 0.002 | 0.0 | 0.031 | -1 | 5 | 1.000 | 1.000 | 0.005 | 0.000 | 6.784 | 0.767 | See | See |
KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)¶All estimators share the following hyperparameters: algorithm=kd_tree.
| estimator | function | n_samples_train | n_samples | n_features | dataset_n_classes | dataset_n_redundant | dataset_random_state | dataset_sample_generator | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_sklearnex | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 2.736 | 0.0 | 0.029 | 0.0 | -1 | 100 | NaN | NaN | 0.795 | 0.0 | 3.443 | 0.0 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 2.780 | 0.0 | 0.029 | 0.0 | 1 | 1 | NaN | NaN | 0.777 | 0.0 | 3.580 | 0.0 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 2.805 | 0.0 | 0.029 | 0.0 | 1 | 100 | NaN | NaN | 0.787 | 0.0 | 3.563 | 0.0 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 2.770 | 0.0 | 0.029 | 0.0 | 1 | 5 | NaN | NaN | 0.804 | 0.0 | 3.446 | 0.0 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 2.789 | 0.0 | 0.029 | 0.0 | -1 | 1 | NaN | NaN | 0.808 | 0.0 | 3.450 | 0.0 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 2.713 | 0.0 | 0.029 | 0.0 | -1 | 5 | NaN | NaN | 0.799 | 0.0 | 3.394 | 0.0 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.759 | 0.0 | 0.021 | 0.0 | -1 | 100 | NaN | NaN | 0.543 | 0.0 | 1.399 | 0.0 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.769 | 0.0 | 0.021 | 0.0 | 1 | 1 | NaN | NaN | 0.524 | 0.0 | 1.469 | 0.0 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.815 | 0.0 | 0.020 | 0.0 | 1 | 100 | NaN | NaN | 0.513 | 0.0 | 1.590 | 0.0 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.791 | 0.0 | 0.020 | 0.0 | 1 | 5 | NaN | NaN | 0.526 | 0.0 | 1.502 | 0.0 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.783 | 0.0 | 0.020 | 0.0 | -1 | 1 | NaN | NaN | 0.522 | 0.0 | 1.498 | 0.0 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.826 | 0.0 | 0.019 | 0.0 | -1 | 5 | NaN | NaN | 0.518 | 0.0 | 1.595 | 0.0 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | dataset_n_classes | dataset_n_redundant | dataset_random_state | dataset_sample_generator | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_sklearnex | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 2.800 | 0.021 | 0.000 | 0.003 | -1 | 100 | 0.974 | 0.977 | 0.654 | 0.008 | 4.284 | 0.063 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.005 | 0.001 | 0.000 | 0.005 | -1 | 100 | 1.000 | 1.000 | 0.002 | 0.001 | 3.490 | 1.422 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.729 | 0.022 | 0.000 | 0.001 | 1 | 1 | 0.960 | 0.967 | 0.117 | 0.004 | 6.231 | 0.292 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 2.529 | 1.482 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 4.694 | 0.041 | 0.000 | 0.005 | 1 | 100 | 0.974 | 0.978 | 0.223 | 0.008 | 21.069 | 0.783 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.003 | 0.001 | 0.000 | 0.003 | 1 | 100 | 1.000 | 1.000 | 0.001 | 0.000 | 5.602 | 2.948 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 1.463 | 0.064 | 0.000 | 0.001 | 1 | 5 | 0.970 | 0.978 | 0.230 | 0.012 | 6.359 | 0.431 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.002 | 0.001 | 0.000 | 0.002 | 1 | 5 | 1.000 | 1.000 | 0.001 | 0.000 | 2.388 | 1.403 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.446 | 0.011 | 0.000 | 0.000 | -1 | 1 | 0.960 | 0.977 | 0.666 | 0.014 | 0.669 | 0.021 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 1 | 1.000 | 1.000 | 0.002 | 0.001 | 1.892 | 0.746 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.855 | 0.015 | 0.000 | 0.001 | -1 | 5 | 0.970 | 0.967 | 0.118 | 0.004 | 7.253 | 0.262 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.004 | 0.001 | 0.000 | 0.004 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 10.362 | 5.813 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.050 | 0.002 | 0.000 | 0.000 | -1 | 100 | 0.989 | 0.988 | 0.008 | 0.001 | 6.292 | 0.756 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 13.984 | 6.902 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.029 | 0.001 | 0.001 | 0.000 | 1 | 1 | 0.980 | 0.975 | 0.001 | 0.000 | 33.775 | 7.638 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 4.913 | 3.343 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.055 | 0.004 | 0.000 | 0.000 | 1 | 100 | 0.989 | 0.990 | 0.001 | 0.000 | 41.661 | 11.161 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 5.257 | 3.366 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.031 | 0.001 | 0.001 | 0.000 | 1 | 5 | 0.988 | 0.990 | 0.001 | 0.000 | 24.331 | 6.519 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 5.049 | 3.428 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.030 | 0.003 | 0.001 | 0.000 | -1 | 1 | 0.980 | 0.988 | 0.008 | 0.001 | 3.681 | 0.699 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 14.662 | 8.681 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.032 | 0.002 | 0.000 | 0.000 | -1 | 5 | 0.988 | 0.975 | 0.001 | 0.000 | 36.649 | 10.079 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | 2 | 0 | 42 | sklearn.datasets.make_classification | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 17.624 | 11.412 | See | See |
KMeans_tall: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | dataset_random_state | dataset_cluster_std | dataset_centers | dataset_sample_generator | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_sklearnex | adjusted_rand_score_sklearnex | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 42 | 20.0 | 200 | sklearn.datasets.make_blobs | 30 | 0.746 | 0.0 | 0.643 | 0.0 | k-means++ | NaN | 30 | NaN | 0.373 | 0.0 | 1.999 | 0.0 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 42 | 20.0 | 200 | sklearn.datasets.make_blobs | 30 | 0.603 | 0.0 | 0.795 | 0.0 | random | NaN | 30 | NaN | 0.319 | 0.0 | 1.889 | 0.0 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 42 | 20.0 | 200 | sklearn.datasets.make_blobs | 30 | 8.777 | 0.0 | 2.734 | 0.0 | k-means++ | NaN | 30 | NaN | 4.348 | 0.0 | 2.019 | 0.0 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 42 | 20.0 | 200 | sklearn.datasets.make_blobs | 30 | 7.915 | 0.0 | 3.032 | 0.0 | random | NaN | 30 | NaN | 4.096 | 0.0 | 1.933 | 0.0 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | dataset_random_state | dataset_cluster_std | dataset_centers | dataset_sample_generator | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_sklearnex | adjusted_rand_score_sklearnex | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 42 | 20.0 | 200 | sklearn.datasets.make_blobs | 30 | 0.002 | 0.0 | 0.255 | 0.000 | k-means++ | 0.001 | 30 | 0.001 | 0.0 | 0.0 | 8.093 | 3.495 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 42 | 20.0 | 200 | sklearn.datasets.make_blobs | 30 | 0.002 | 0.0 | 0.000 | 0.002 | k-means++ | 1.000 | 30 | 1.000 | 0.0 | 0.0 | 9.413 | 5.164 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 42 | 20.0 | 200 | sklearn.datasets.make_blobs | 30 | 0.002 | 0.0 | 0.250 | 0.000 | random | 0.001 | 30 | 0.001 | 0.0 | 0.0 | 8.278 | 3.202 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 42 | 20.0 | 200 | sklearn.datasets.make_blobs | 30 | 0.002 | 0.0 | 0.000 | 0.002 | random | 1.000 | 30 | 1.000 | 0.0 | 0.0 | 9.625 | 6.471 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 42 | 20.0 | 200 | sklearn.datasets.make_blobs | 30 | 0.002 | 0.0 | 11.221 | 0.000 | k-means++ | 0.002 | 30 | 0.001 | 0.0 | 0.0 | 6.693 | 2.828 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 42 | 20.0 | 200 | sklearn.datasets.make_blobs | 30 | 0.002 | 0.0 | 0.013 | 0.002 | k-means++ | 1.000 | 30 | 1.000 | 0.0 | 0.0 | 9.760 | 5.009 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 42 | 20.0 | 200 | sklearn.datasets.make_blobs | 30 | 0.002 | 0.0 | 11.606 | 0.000 | random | 0.001 | 30 | 0.002 | 0.0 | 0.0 | 6.077 | 3.170 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 42 | 20.0 | 200 | sklearn.datasets.make_blobs | 30 | 0.002 | 0.0 | 0.014 | 0.002 | random | 1.000 | 30 | 1.000 | 0.0 | 0.0 | 9.053 | 6.681 | See | See |
KMeans_short: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | dataset_random_state | dataset_cluster_std | dataset_centers | dataset_sample_generator | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_sklearnex | adjusted_rand_score_sklearnex | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 42 | 20.0 | 200 | sklearn.datasets.make_blobs | 20 | 0.344 | 0.0 | 0.009 | 0.0 | k-means++ | NaN | 20 | NaN | 0.058 | 0.0 | 5.979 | 0.0 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 42 | 20.0 | 200 | sklearn.datasets.make_blobs | 20 | 0.108 | 0.0 | 0.030 | 0.0 | random | NaN | 20 | NaN | 0.148 | 0.0 | 0.726 | 0.0 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 42 | 13.0 | 200 | sklearn.datasets.make_blobs | 20 | 1.204 | 0.0 | 0.133 | 0.0 | k-means++ | NaN | 20 | NaN | 0.286 | 0.0 | 4.212 | 0.0 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 42 | 13.0 | 200 | sklearn.datasets.make_blobs | 20 | 0.353 | 0.0 | 0.453 | 0.0 | random | NaN | 20 | NaN | 0.721 | 0.0 | 0.490 | 0.0 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | dataset_random_state | dataset_cluster_std | dataset_centers | dataset_sample_generator | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_sklearnex | adjusted_rand_score_sklearnex | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 42 | 20.0 | 200 | sklearn.datasets.make_blobs | 20 | 0.002 | 0.0 | 0.133 | 0.000 | k-means++ | 0.001 | 20 | 0.000 | 0.001 | 0.0 | 3.055 | 0.662 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 42 | 20.0 | 200 | sklearn.datasets.make_blobs | 20 | 0.002 | 0.0 | 0.000 | 0.002 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.183 | 4.552 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 42 | 20.0 | 200 | sklearn.datasets.make_blobs | 20 | 0.002 | 0.0 | 0.134 | 0.000 | random | 0.002 | 20 | -0.001 | 0.001 | 0.0 | 3.144 | 0.750 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 42 | 20.0 | 200 | sklearn.datasets.make_blobs | 20 | 0.002 | 0.0 | 0.000 | 0.002 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 8.923 | 6.749 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 42 | 13.0 | 200 | sklearn.datasets.make_blobs | 20 | 0.004 | 0.0 | 4.560 | 0.000 | k-means++ | 0.216 | 20 | 0.359 | 0.002 | 0.0 | 2.146 | 0.255 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 42 | 13.0 | 200 | sklearn.datasets.make_blobs | 20 | 0.002 | 0.0 | 0.008 | 0.002 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.690 | 4.256 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 42 | 13.0 | 200 | sklearn.datasets.make_blobs | 20 | 0.004 | 0.0 | 4.523 | 0.000 | random | 0.270 | 20 | 0.319 | 0.002 | 0.0 | 2.080 | 0.290 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 42 | 13.0 | 200 | sklearn.datasets.make_blobs | 20 | 0.002 | 0.0 | 0.008 | 0.002 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 8.165 | 3.832 | See | See |
LogisticRegression: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)¶All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
| estimator | function | n_samples_train | n_samples | n_features | dataset_n_classes | dataset_n_informative | dataset_n_redundant | dataset_random_state | dataset_sample_generator | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | 5 | 5 | 0 | 42 | sklearn.datasets.make_classification | [20] | 17.497 | 0.0 | [-0.06743391] | 0.000 | NaN | NaN | NaN | NaN | NaN | 3.137 | 0.0 | 5.577 | 0.0 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | 5 | 5 | 0 | 42 | sklearn.datasets.make_classification | [26] | 1.374 | 0.0 | [1.51358758] | 0.001 | NaN | NaN | NaN | NaN | NaN | 1.214 | 0.0 | 1.132 | 0.0 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | dataset_n_classes | dataset_n_informative | dataset_n_redundant | dataset_random_state | dataset_sample_generator | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | 5 | 5 | 0 | 42 | sklearn.datasets.make_classification | [20] | 0.000 | 0.000 | [41.12977318] | 0.0 | NaN | NaN | NaN | NaN | 0.533 | 0.000 | 0.0 | 0.862 | 0.488 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | 5 | 5 | 0 | 42 | sklearn.datasets.make_classification | [20] | 0.000 | 0.000 | [0.17582321] | 0.0 | NaN | NaN | NaN | NaN | 1.000 | 0.000 | 0.0 | 0.302 | 0.205 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | 5 | 5 | 0 | 42 | sklearn.datasets.make_classification | [26] | 0.003 | 0.001 | [67.52632658] | 0.0 | NaN | NaN | NaN | NaN | 0.310 | 0.004 | 0.0 | 0.731 | 0.153 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | 5 | 5 | 0 | 42 | sklearn.datasets.make_classification | [26] | 0.000 | 0.000 | [13.21045073] | 0.0 | NaN | NaN | NaN | NaN | 0.000 | 0.001 | 0.0 | 0.168 | 0.115 | See | See |
Ridge: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)¶All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.
| estimator | function | n_samples_train | n_samples | n_features | dataset_n_informative | dataset_random_state | dataset_sample_generator | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | 10 | 42 | sklearn.datasets.make_regression | NaN | 0.337 | 0.0 | 0.238 | 0.0 | NaN | NaN | NaN | 0.338 | 0.0 | 0.997 | 0.0 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | 10 | 42 | sklearn.datasets.make_regression | NaN | 1.786 | 0.0 | 0.448 | 0.0 | NaN | NaN | NaN | 0.409 | 0.0 | 4.366 | 0.0 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | dataset_n_informative | dataset_random_state | dataset_sample_generator | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_sklearnex | stdev_sklearnex | speedup | stdev_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Ridge | predict | 1000 | 1000 | 10000 | 10 | 42 | sklearn.datasets.make_regression | NaN | 0.015 | 0.002 | 5.499 | 0.0 | NaN | NaN | 0.088 | 0.021 | 0.002 | 0.694 | 0.125 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | 10 | 42 | sklearn.datasets.make_regression | NaN | 0.000 | 0.000 | 0.637 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.735 | 0.443 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | 10 | 42 | sklearn.datasets.make_regression | NaN | 0.000 | 0.000 | 3.339 | 0.0 | NaN | NaN | 1.000 | 0.000 | 0.000 | 0.843 | 0.518 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | 10 | 42 | sklearn.datasets.make_regression | NaN | 0.000 | 0.000 | 0.008 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.666 | 0.560 | See | See |